Szczegóły publikacji
Opis bibliograficzny
Predictive monitoring of workforce dynamics via neural networks / Jakub Nowak, Marcin Korytkowski, Rafał SCHERER, Błażej Żak, Zorza Tymorek, Anita Zbieg // W: Artificial Intelligence and Soft Computing : 24th International Conference, ICAISC 2025 : Zakopane, Poland, June 22–26, 2025 : proceedings , Pt. 2 / eds. Leszek Rutkowski, [et al.]. — Cham : Springer Nature Switzerland, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; 15949 ). — ISBN: 978-3-032-03707-7; e-ISBN: 978-3-032-03708-4. — S. 364–373. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-11-01. — R. Scherer - dod. afiliacja: Czȩstochowa University of Technology
Autorzy (6)
- Nowak Jakub
- Korytkowski Marcin
- AGHScherer Rafał
- Żak Błażej
- Tymorek Zorza
- Zbieg Anita
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164438 |
|---|---|
| Data dodania do BaDAP | 2026-01-22 |
| DOI | 10.1007/978-3-032-03708-4_30 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Artificial Intelligence and Soft Computing 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Effective human resource management requires continuous monitoring of workforce dynamics, including role transitions, promotions, and structural changes within an organization. This paper presents a solution based on recurrent neural networks (RNN), utilizing LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) architectures to analyze sequential data derived from employee interactions in a large organizational environment. The research was conducted using a text-based dataset of approximately 184 GB, encompassing various communication formats from emails and meeting transcripts to team discussions while incorporating organizational hierarchy context. The proposed model detects significant personnel events, such as changes in supervisors, promotions, or positional shifts. The analysis considers 16 features describing relationships between employees and their organizational surroundings. The use of LSTM and GRU architectures enabled the capture of complex temporal dependencies and accurate classification of career-related behavioral patterns. Designed for near real-time operation, the system supports the rapid identification of potential anomalies and assists managerial decision-making. This approach may be applied in both private and public sector institutions, wherever workforce management and information security are of strategic importance.
